mlsanityData to 7 Oct 2026

A Review of Deep Reinforcement Learning for Smart Building Energy Management

arXiv 2008.05074 ↗First posted 12 Aug 2020eess.SY cs.SY eess.SPcited by 35

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Abstract (arXiv)

Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy-efficient and green buildings. However, it is a nontrivial task due to the following challenges. Firstly, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Secondly, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Thirdly, there are many spatially and temporally coupled operational constraints. Fourthly, building energy optimization problems can not be solved in real-time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this paper provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Aug 2020.

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Citing papers per year: 2021: 2; 2022: 9; 2023: 5; 2024: 6; 2025: 7; 2026: 6.

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  1. 2022PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithmcited by 51
  2. 2022Controlling Commercial Cooling Systems Using Reinforcement Learningcited by 25
  3. 2025Pareto Set Learning for Multi-Objective Reinforcement Learningcited by 18
  4. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  5. 2022Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Managementcited by 11
  6. 2023State-Wise Safe Reinforcement Learning With Pixel Observationscited by 9
  7. 2023Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learningcited by 7
  8. 2025Foundation Models for CPS-IoT: Opportunities and Challengescited by 5
  9. 2023A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildingscited by 4
  10. 2024The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainabilitycited by 4
  11. 2022Low Emission Building Control with Zero-Shot Reinforcement Learningcited by 3
  12. 2024An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC controlcited by 3
23 more
  1. 2022Decision-making with Speculative Opponent Modelscited by 2
  2. 2024Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Surveycited by 2
  3. 2026Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Modelscited by 2
  4. 2022Machine Learning for Smart and Energy-Efficient Buildingscited by 1
  5. 2023Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approachcited by 1
  6. 2025Data-Driven Policy Mapping for Safe RL-based Energy Management Systemscited by 1
  7. 2021Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?cited by 0
  8. 2021Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governancecited by 0
  9. 2022Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learningcited by 0
  10. 2022Low Emission Building Control with Zero-Shot Reinforcement Learningcited by 0
  11. 2022Conv-NILM-Net, a causal and multi-appliance model for energy source separationcited by 0
  12. 2023The Benefit of Noise-Injection for Dynamic Gray-Box Model Creationcited by 0
  13. 2024Experimental evaluation of offline reinforcement learning for HVAC control in buildingscited by 0
  14. 2024Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Cameracited by 0
  15. 2024Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literaturecited by 0
  16. 2025Decision-Focused Learning for Complex System Identification: HVAC Management System Applicationcited by 0
  17. 2025Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learningcited by 0
  18. 2025BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learningcited by 0
  19. 2026Is Pure Exploitation Sufficient in Exogenous MDPs with Linear Function Approximation?cited by 0
  20. 2026LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchycited by 0
  21. 2026Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibilitycited by 0
  22. 2026Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildingscited by 0
  23. 2026From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensingcited by 0